Multimodal AI is a class of artificial intelligence model that processes several types of input at once, text, images, tables and structured data, inside a single reasoning pass. In finance, it matters because the raw material of the job is rarely clean text: it arrives as scanned PDFs, phone photos of delivery notes, spreadsheet attachments and email bodies that reference all three.
Classic optical character recognition works in stages. It converts pixels into characters, then a second layer tries to guess the structure from the character positions. Layout is inferred, never read. That pipeline is brittle on exactly the documents finance receives most: a supplier invoice with a two-page line-item table, a delivery note photographed at an angle in a warehouse, a handwritten annotation in the margin, a stamp overlapping a total.
A multimodal model reads the page as a page. It sees the visual layout and the text simultaneously, so it understands that a number belongs to a column because of where it sits, that a table continues on the next page, and that a handwritten line is an annotation rather than a line item. The gain is not speed. It is the number of documents that make it through without a human touching them.
This is what sits underneath Phacet's intelligent document processing and document parsing capabilities. Extracted fields land in a Table with configurable AI columns, each output carrying a confidence score and a link back to the exact region of the source document. The accounting inbox agent applies it to whatever arrives by email, whatever the format.
Multimodal reading is the structuring step. It is what makes the data and documents layer reliable enough for control to be worth running on top of it.
